CN113111480B - Method and device for diagnosing and detecting running state of drainage pipe network - Google Patents
Method and device for diagnosing and detecting running state of drainage pipe network Download PDFInfo
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Abstract
The invention provides a method and a device for diagnosing the running state of a drainage pipe network. The device can collect angular acceleration and axial acceleration data according to the characteristic that an MEMS gyroscope accelerometer in the instrument can change angular acceleration and axial acceleration along with the change of the motion state of water flow, so that secondary integral displacement superposition is carried out, the water flow motion trail of a drainage pipe network is reproduced, an established model base is compared, the operation state of the water pipe network is diagnosed, and the device has the functions of detecting and positioning abnormal operation conditions such as breakage, pipe breakage, siltation and the like in the drainage pipe. The method can be applied to the detection of the current situation of the drainage pipe network, the mastering and evaluation of the running state of the drainage pipe network, the urban waterlogging prevention and control, the black and odorous river treatment and the like, and has wide application prospect.
Description
Technical Field
The invention belongs to the technical field of municipal drainage pipe network management systems, and particularly relates to a method and a device for diagnosing and detecting the running state of a drainage pipe network.
Background
Urban drainage pipe network system, deeply buried underground, "invisible, untouched", because attach attention to inadequately, the management lags behind, the urban waterlogging, sewage treatment inefficiency and black and odorous water body scheduling problem are more and more outstanding in recent years. By 5 months in 2018, the number of black and odorous water bodies determined in China is 2100, and the total flow area is 1463km 2 The black and odorous problem of the urban inland river is more serious, the sensory understanding of urban residents on the environmental quality is seriously influenced, and people are provided with the black and odorous urban inland riverThe life of (2) brings great influence. However, the urban drainage pipe network has insufficient diagnosis technology for problems such as silting, collapse, leakage, and infiltration of peripheral groundwater, and the operation state of the drainage pipe network is difficult to grasp and evaluate. The existing drainage pipe network health diagnosis and detection methods can be divided into two categories, namely an instrument detection method and an on-line monitoring method.
(1) Instrumental detection method
Summary of the method: the instrument detection method mainly comprises manual detection and partial instrument detection. The manual detection method is a method of observation by naked eyes of inspectors or by means of a detector, and specifically comprises methods of visual inspection, reflector inspection, pipeline inspection under divers, mud measuring bucket detection and the like.
The advantages and disadvantages are as follows: the instrument inspection technology has the advantages of clear images, high safety, repeatable image viewing and the like, but also has the problems of inaccurate positioning, difficulty in adapting to the pipeline silting environment, high cost, low efficiency and the like.
(2) On-line monitoring method
Summary of the method: by applying a large number of computers and automatic monitoring technologies, the system realizes the whole-process monitoring and management of the underground drainage pipe network system in a central control room, establishes an urban flood intelligent monitoring system, and can timely master, early warn and predict the urban flood disaster condition.
The advantages and disadvantages are as follows: the method can only be used for diagnosing abnormal inflow water flow in a drainage system, but the conditions such as siltation, collapse and the like cannot be identified, and the problems of complex monitoring environment, low data accuracy and the like exist.
Disclosure of Invention
Aiming at the defects, the invention provides the method and the device for diagnosing and detecting the running state of the drainage pipe network, which can realize wireless detection, have small instrument volume and can simultaneously detect and diagnose a large pipe network.
The invention provides the following technical scheme: a method for diagnosing and detecting the running state of drainage pipe network for detecting and locating the damaged, broken and deposited failure pipe sections of municipal drainage pipeline includes such steps as:
1) assembling a debugging detection device, putting the pipe network pipeline starting point inspection well to be detected into the inspection well, positioning according to a computer terminal GPS, and recovering a detector after detection is finished;
2) taking out the SD card, reading and processing data, and extracting X, Y, Z triaxial axial acceleration and angular acceleration recorded by the MEMS in the detector;
3) Reproducing the flow path of the detector, and drawing X, Y, Z a time-varying line graph of the three-axis axial acceleration and the angular acceleration;
4) matching the processed information with the established model, and diagnosing abnormal operation conditions in the drainage pipe network;
the data stored in the SD card comprise time data, 3D acceleration data and 3D angular acceleration data, wherein the 3D acceleration data comprise g x 、g y 、g z The 3D angular acceleration data comprises w x 、w y 、w z The time data, the 3D acceleration data and the 3D angular acceleration data form a frame of data by 7 data, the detector collects and records the frame of data every 100ms, and the obtained 7 data are subjected to coordinate conversion and displacement compensation;
and then establishing black box model and time series three-dimensional track pattern matching.
Further, in the coordinate conversion process, the collected and processed 3D acceleration is subjected to primary integration to obtain the moving speed, further, secondary integration is performed to obtain the displacement, the displacement is popularized to a three-dimensional space, and t n The motion displacement along the directions of the x, y and z axes of the acceleration sensor at the moment is respectively as follows:
wherein v (n) is t n Instantaneous speed at time, a (n) being t n Instantaneous acceleration at the time, s (n) is 0 to t n The time period is shifted. In a three-dimensional coordinate system, spatial position coordinate points at all times are connected to obtain a spatial motion track.
Further, the coordinate transformation is to derive a spatial trajectory, convert the measured acceleration to a constant referenceIn the coordinate system, the geographic coordinate system of the target object is set as ox n y n z n And the carrier coordinate system of the sensor is ox b y b z b And the transformation relation obtained from the geographic coordinate system to the carrier coordinate system by the rotating sequence of the Z, X and Y axes is as follows:
in the formula
Is a rotation matrix, i.e., a direction cosine matrix.
Further, the displacement compensation is to firstly perform normalization processing on the collected acceleration data, convert the attitude quaternion calculated last time into a carrier coordinate system, obtain a gravity unit vector of the current carrier coordinate system, and the error vector of each quantity is an error between the attitude after the gyro is integrated and the attitude measured by the accelerometer, so as to correct the gyroscope, that is:
E(n)＝E(n-1)+K i ×e
g(n)＝g(n-1)+K p ×e+E(n)
wherein g ═ g (g) x ，g y ，g z ) For gyroscope angular velocity data, by adjusting K i And K p Both parameters enable a fast correction of the gyroscope angular velocity data by means of acceleration data,
according to the ideal condition that the speed before the motion starts and after the motion stops is zero, the acceleration and the displacement before the motion starts are also zero, namely v r (0)＝0，v r (k e )＝0，a r (0)＝0，s r (0)＝0，k e Is the end time of the exercise. The compensation polynomial for velocity and displacement is:
wherein: v (k), s (k) are calculation results before reconstruction; v is r (k)、s r (k) The speed and displacement after reconstruction; a (0), v (0) and s (0) are system initial states, and k is e Is the movement time of the system.
Furthermore, the black box model building method comprises the steps of building different black box models aiming at different unconventional operation states such as siltation, collapse, leakage and peripheral groundwater infiltration, building a multidimensional black box model with changed pipe diameter, siltation height, siltation volume, flow rate and motion track in the siltation state, building a multidimensional black box model with changed pipe diameter, sublevel height and motion track in the pipe disjointed state, and building a black box model with changed pipe diameter, disjointed height and motion track in the water leakage state, wherein the black box model in the water leakage state comprises parameters such as pipe diameter, damaged area and motion track; the water leakage includes both leak-in and leak-out conditions.
Further, in the time series three-dimensional track pattern matching process, because the motion track is a multi-point pattern changing along with time, the time series point pattern matching problem of the three-dimensional space is taken as a problem analysis in the two-dimensional space, the point set in the whole three-dimensional space is projected to three planes of XOY, YOZ and XOZ respectively, track similarity distance measurement functions are calculated for the two-dimensional point sets on the three planes respectively, and finally the feature similarity of two points in the three-dimensional space is obtained by adding the feature similarities of the three two-dimensional spaces.
Further, the two-dimensional point set pattern matching comprises the following steps:
s1: let T be a trajectory of a point consisting of two-dimensional coordinates representing a position, defined as a candidate trajectory in the database, T ═ s 1 ，s 2 ，...s n }，s i (1. ltoreq. i. ltoreq.n) represents a two-dimensional coordinate point. The length of the track T is n; defining the target track as q ═ t 1 ，t 2 ，...t n Given a trajectory T, its sub-trajectories from i point to j point are T [ i, j]＝s i ，S i+1 ，...S j ；
S2: to pairIn a target track point set q ═ t 1 ，t 2 ，...t n And a trace point t k E.g. q, trace point t k And sub-track T [ i, j]The shortest distance between them is:
d(T[i，j]，t k )＝min i≤h≤j {d(s h ，t k )}
wherein d(s) i ，t j ) Representing points of track s i And the locus point t j The euclidean distance between them.
Furthermore, in order to match the track, two function distances are defined for judgment, and the "sum function" distance is mainly used for calculating each track point T in each segment pair target track point set q of the candidate track T j The sum of the shortest distances of the target track point set q and the candidate track T is measured according to the sum of the shortest distances of the target track point set q and the candidate track T; the distance (Maxdis) of the maximum function is calculated for each track point T in the target track point set q of each segment pair of the candidate track T j Is used as the distance between the target track point set q and the candidate track T.
Definition 1: "sum function" distance:
definition 2: distance of "maximum function": maxdis (T, q) ═ max 1≤j≤m d(T[l i .l i+1 ]，t j )；
Wherein 1 is less than or equal to l 1 ≤…≤l m ≤l m+1 ≤n；
S3: and analyzing the 'sum function' distance and the 'maximum function' distance, knowing that the two function distances are both to divide the candidate track T into m sections, wherein m is the number of the midpoints of the target track, then taking the minimum distance from a plurality of points in the candidate track sections to the corresponding candidate track points, and finally adding the minimum distances. The process is realized by adopting pruning dynamic programming, and if the target track q has m points and the candidate tracks have n points, the m distances are compared, and the m distances are compared through a distance detection function in sequence.
The invention also provides a device for diagnosing and detecting the running state of the drainage pipe network by adopting the method, which comprises a bottom cover and a top cover, wherein the bottom cover and the top cover form a sealed detector internal space through a waterproof sealing ring, and the internal space sequentially comprises a counterweight system, an MEMS gyroscope acceleration system, a starting system and a polymer lithium battery from bottom to top;
the counterweight system comprises an x-direction eccentric counterweight block and a bottom cover integral counterweight block; the MEMS gyroscope acceleration system comprises an MEMS gyroscope accelerometer and a GPS chip which are arranged on the same SD storage card; the starting system comprises a starter and a starter fixing groove; the MEMS gyroscope accelerometer adopts a high-precision gyroscope accelerometer WT61SD and is a six-axis module; when debugging is carried out, debugging information is required to be output through a serial port, the acceleration of the MEMS gyroscope and the USB-TTL are well connected, and then the MEMS gyroscope is inserted into a computer; the SD memory card has 16G or 32G storage capacity.
Further, the overall height of the detection device is 2 cm.
The beneficial effects of the invention are as follows:
1. according to the method and the detector for diagnosing the running state of the drainage pipe network, provided by the invention, the running state of the pipe network is analyzed by adopting the MEMS sensor as a main body through recording the water flow state and reproducing the posture, the detection period is short, and the authenticity is reliable.
2. The method and the detector for diagnosing the running state of the drainage pipe network can realize wireless detection, have smaller integral instrument volume, are easier to carry in operation compared with detection equipment in the current market, have wide coverage range of detectable pipelines, and can simultaneously detect and diagnose a large pipe network.
3. The method and the detector for diagnosing the running state of the drainage pipe network greatly reduce the labor cost, have lower equipment cost, greatly reduce the overall cost compared with other equipment in the current market, are economical and can greatly reduce the cost required by the detection of the drainage pipe network.
4. The method and the detector for diagnosing the running state of the drainage pipe network can realize positioning and collecting a large amount of effective data such as the hydraulic characteristics of water flow in pipelines, the flow change of the drainage pipe network and the like in one step.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the drawings.
Wherein:
fig. 1 is a schematic flow chart of a method for diagnosing and detecting an operating state of a drain pipe network according to the present invention;
FIG. 2 is a schematic diagram of a geographic coordinate system during coordinate transformation of the diagnostic test method provided by the present invention;
FIG. 3 is a schematic diagram of coordinate system transformation of the diagnostic test method provided by the present invention;
FIG. 4 is a black box model established by the diagnostic test method of the present invention;
FIG. 5 is an exploded view of the diagnostic detector for the operation status of the drainage pipe network according to the present invention;
FIG. 6 is a schematic diagram of a MEMS gyroscope accelerometer provided in accordance with the present invention;
fig. 7 is a schematic block diagram of a diagnostic detector for the operation state of a drain pipe network according to the present invention.
Detailed description of the preferred embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, a method for diagnosing and detecting an operating condition of a drainage pipe network, which is used for detecting and positioning an abnormal operating condition point of the drainage pipe network, comprises the following steps:
1) assembling an inspection tester and putting the inspection tester into an inspection well of a pipeline starting point to be detected, fishing and recovering after detection is finished, reading data and preprocessing the data;
the detector comprises an MEMS gyroscope accelerometer system, a polymer lithium battery power supply system for supplying power to the whole device, a starting system, a GPS positioning system, an SD card data collection circuit system, an instrument gravity center adjusting counterweight system and an appearance packaging and sealing system;
the MEMS gyroscope accelerometer system adopts a six-axis module and a high-precision gyroscope accelerometer WT61SD, debugging information needs to be output through a serial port during debugging, the module is connected with a USB-TTL, and then the module is plugged into a computer;
the starting system adopts a switch controlled by a radio signal, is fixed by using a fixed card slot, and the starting device can receive a starting signal after being packaged;
the polymer lithium battery system employs a rechargeable large-capacity lithium battery, and since the overall power consumption will be large, the polymer lithium battery is required.
The MEMS gyroscope accelerometer system, the polymer lithium battery power supply system for supplying power to the whole device and the starting system are connected in the same way: the lithium battery is positioned above, the clamping groove for fixing the switch device is positioned in the middle, the gyro accelerometer WT61SD is positioned below, the lithium battery, the switch device and the gyro accelerometer WT61SD are connected in series, and the lithium battery is used for controlling power supply of the gyro accelerometer WT61 SD;
The SD storage system is directly realized by a 16G or 32G SD card, the gyro accelerometer WT61SD is provided with an SD card slot, and the SD card is used for receiving and storing a large amount of data;
the GPS positioning system directly adopts a commercially available GPS chip and is directly adhered to the front surface of the counterweight plate. The GPS positioning chip can generate longitude and latitude signals discontinuously, so that a computer terminal can monitor the specific position of the detector for the abnormal operation condition of the drainage pipe network in real time;
the instrument gravity center adjusting counterweight system consists of a counterweight plate and counterweight balls, the counterweight balls are adhered to the reverse side of the counterweight plate, and the gravity center and the weight of the whole instrument are ensured by adjusting the positions and the weights of the counterweight balls;
appearance packing sealing system is cavity plastic ball, and the equipment is the leakproofness that uses the rubber band to increase the spheroid, and separation water infiltration, appearance packing sealing system should be removable moreover, takes out the SD memory card behind the later stage recovery unit of being convenient for.
3D angular acceleration and 3D acceleration data messy code file records generated in the motion process of the detector are stored in an SD card, and the stored TXT messy code file is processed and converted into a 16-system TXT file by using software UltraEdit;
the mini SD memory card stores records: 1) time; 2)3D acceleration; 3)3D angular velocity; these 7 data form one frame of data, and 1 frame of data is collected and recorded every 100 ms.
The data stored in the SD card comprise time data, 3D acceleration data and 3D angular acceleration data, wherein the 3D acceleration data comprise g x 、g y 、g z The 3D angular acceleration data comprises w x 、w y 、w z The time data, the 3D acceleration data and the 3D angular acceleration data form a frame of data by 7 data, the detector collects and records a frame of data every 100ms, and the obtained 7 data are subjected to coordinate conversion and displacement compensation;
and then establishing black box model and time series three-dimensional track pattern matching.
Wherein the coordinate transformation comprises the following steps:
and performing primary integration on the collected and processed 3D acceleration to obtain the moving speed, and further performing secondary integration to obtain the displacement. Generalizing to three-dimensional space, t n The motion displacement along the directions of the x, y and z axes of the acceleration sensor at the moment is respectively as follows:
wherein v (n) is t n Instantaneous speed at time, a (n) being t n Instantaneous acceleration at the time, s (n) is 0 to t n And (5) time interval displacement, and connecting spatial position coordinate points at all times in a three-dimensional coordinate system to obtain a spatial motion track.
However, since the direction of the object changes during the spatial motion, the three-axis coordinate system in which the sensor is located also changes, and in order to obtain the spatial trajectory, the measured acceleration needs to be converted into a constant reference coordinate system.
In order to derive the spatial trajectory, the measured acceleration is converted into a constant reference coordinate system. Setting the geographic coordinate system of the target object as ox n y n z n And the carrier coordinate system of the sensor is ox b y b z b . The transformation relation obtained by the rotation sequence of the Z, X and Y axes from the geographic coordinate system to the carrier coordinate system is as follows:
as shown in FIGS. 2-3, wherein
Is a rotation matrix, i.e., a direction cosine matrix.
The micro-mechanical electronic system can generate integral accumulated errors in the attitude calculation process, so that the attitude angle is diverged. It needs to be compensated for displacement and speed. Firstly, normalizing acquired acceleration data, converting the attitude quaternion calculated last time into a carrier coordinate system to obtain a gravity unit vector of the current carrier coordinate system, wherein an error vector of each quantity is an error between an attitude after the gyro is integrated and an attitude measured by an accelerometer and is used for correcting the gyroscope, namely:
E(n)＝E(n-1)+K i ×e
g(n)＝g(n-1)+K p ×e+E(n)
wherein g ═ g (g) x ，g y ，g z ) For gyroscope angular velocity data, by adjusting K i And K p Both parameters enable a fast correction of the gyroscope angular velocity data by means of acceleration data,
let the velocity quadratic polynomial be:
v r (k)＝v(k)+b 1 k 2 +b 2 k+b 3
the displacement polynomial is then:
according to a r (0) The velocity binomial is derived as 0:
a r (k)＝a(k)+2b 1 k+b 2
According to the ideal condition that the speed before the motion starts and after the motion stops is zero, the acceleration and the displacement before the motion starts are also zero, namely v r (0)＝0，v r (k e )＝0，a r (0)＝0，s r (0)＝0，k e Is the end time of the exercise.
The following can be obtained:
b 2 ＝-a(0)
b 3 ＝-v(0)
b 4 ＝-s(0)
the compensation polynomial for velocity and displacement is:
wherein: v (k), s (k) are the results of the calculations before reconstruction, v r (k)、s r (k) For the reconstructed velocity and displacement, a (0), v (0), and s (0) are the initial states of the system.
As shown in fig. 4, the black box model is established by establishing different black box models according to different unconventional operation states such as siltation, collapse, leakage, and peripheral groundwater infiltration, the siltation state is established by establishing a multidimensional black box model with changed pipe diameter, siltation height, siltation volume, flow rate, and motion trajectory, the pipe disjointed state is established by establishing a multidimensional black box model with changed pipe diameter, disjointed height, and motion trajectory, and the black box model in the water leakage (leakage and leakage) state includes parameters such as pipe diameter, damaged area, and motion trajectory.
In the time series three-dimensional track pattern matching process, because the motion track is a multi-point pattern which changes along with time, the time series point pattern matching problem of a three-dimensional space is taken as the problem analysis in a two-dimensional space, the point set in the whole three-dimensional space is respectively projected to three planes of XOY, YOZ and XOZ, the track similarity distance measurement function is respectively calculated for the two-dimensional point set on the three planes, and finally the feature similarity of two points in the three-dimensional space is obtained by adding the feature similarities of the three two-dimensional spaces.
The two-dimensional point set pattern matching comprises the following steps:
s1: let T be a trajectory of a point consisting of two-dimensional coordinates representing a position, defined as a candidate trajectory in the database, T ═ s 1 ，s 2 ，...s n }，s i (1. ltoreq. i. ltoreq.n) represents a two-dimensional coordinate point. The length of the track T is n. Defining the target track as q ═ t 1 ，t 2 ，...t n Given a trajectory T, its sub-trajectories from i point to j point are T [ i, j]＝s i ，S i+1 ，...S j ；
S2: for a target trajectory point set q ═ t 1 ，t 2 ，...t n And a trace point t k E.g. q, trace point t k And sub-track T [ i, j]The shortest distance between them is:
d(T[i，j]，t k )＝min i≤h≤j {d(s h ，t k )}
wherein d(s) i ，t j ) Representing points of track s i And the locus point t j The euclidean distance between them.
Time series three-dimensional track pattern matching for track matching, two function distances are defined for judgment, and the sum function distance (SumDis) is mainly used for calculating each track point T in each segment pair target track point set q of candidate track T j The sum of the shortest distances of the target track point set q and the candidate track T is measured according to the sum of the shortest distances of the target track point set q and the candidate track T; "maximum letterThe distance (MaxDis) is calculated by calculating each track point T in the target track point set q of each segment pair of the candidate track T j Is used as the distance between the target track point set q and the candidate track T.
Definition 1: "sum function" distance:
definition 2: distance of "maximum function": maxdis (T, q) ═ max 1≤j≤m d(T[l i .l i+1 ]，t j )；
Wherein 1 is less than or equal to l 1 ≤…≤l m ≤l m+1 ≤n；
S3: and analyzing the 'sum function' distance and the 'maximum function' distance, and knowing that the two function distances are both to divide the candidate track T into m sections (m is the number of the midpoints of the target track), then taking the minimum distance from a plurality of points in the candidate track section to the corresponding candidate track point, and finally adding the minimum distances. The process is realized by adopting pruning dynamic programming, and if the target track q has m points and the candidate tracks have n points, the m distances are compared, and the m distances are compared through a distance detection function in sequence.
Example 2
As shown in fig. 5-7, the diagnostic detector for the running state of the drain pipe network provided by the invention comprises a bottom cover 1 and a top cover 9, wherein the bottom cover 1 and the top cover 9 form a sealed detector internal space through a waterproof sealing ring 10, and the internal space sequentially comprises a counterweight system, an MEMS gyroscope acceleration system, a starting system and a polymer lithium battery 8 from bottom to top.
The counterweight system comprises an x-direction eccentric counterweight block 2 and a bottom cover integral counterweight block 3.
The MEMS gyroscope acceleration system comprises an MEMS gyroscope accelerometer 4 and a GPS chip 5 which are arranged on the same SD storage card.
The MEMS gyroscope accelerometer adopts a high-precision gyroscope accelerometer WT61SD and is a six-axis module; during debugging, debugging information needs to be output through a serial port, the acceleration of the MEMS gyroscope and the USB-TTL are well connected, and then the MEMS gyroscope is plugged into a computer.
The starter system comprises a starter 6 and a starter securing slot 7.
The starting system adopts a switch controlled by a radio signal and is fixed by a fixed card slot, and the starting device can receive a starting signal after being packaged.
The polymer lithium battery system employs a rechargeable large-capacity lithium battery, and since the overall power consumption will be large, the polymer lithium battery is required.
The MEMS gyroscope acceleration system, the polymer lithium battery power supply system for supplying power to the whole device and the starting system are connected in the same way: the polymer lithium battery 8 is located the top, and the draw-in groove of fixed switch ware is located the centre, and top accelerometer WT61SD is located the below, and the three connects in series, realizes through the switch starter that the lithium battery supplies power control to top accelerometer WT61 SD.
The SD storage system is directly realized by a 16G or 32G SD card, the gyro accelerometer WT61SD is provided with an SD card slot, and the SD card is used for receiving and storing a large amount of data.
The GPS positioning system directly adopts a commercially available GPS chip and is directly adhered to the front surface of the counterweight plate. The GPS positioning chip can generate longitude and latitude signals discontinuously, so that the computer terminal can monitor the specific position of the detector for the abnormal operation condition of the drainage pipe network in real time
The instrument gravity center adjusting counterweight system consists of a counterweight plate and counterweight balls, wherein the counterweight balls are adhered to the reverse side of the counterweight plate, and the gravity center and the weight of the whole instrument are ensured by adjusting the positions and the weights of the counterweight balls.
Appearance packing sealing system is cavity plastic ball, and the equipment is the leakproofness that uses the rubber band to increase the spheroid, and separation water infiltration, and appearance packing sealing system should be dismantled moreover, takes out the SD memory card behind the later stage recovery unit of being convenient for, and the analysis is constructed the model and is compared the diagnosis on the terminal information data processing system of developing by oneself.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (4)
1. A method for diagnosing and detecting the running state of a drainage pipe network is used for detecting and positioning the damaged, broken and silted fault pipe sections of municipal drainage pipelines and is characterized by comprising the following steps:
1) Assembling a debugging detection device, putting the pipe network pipeline starting point inspection well to be detected into the inspection well, positioning according to a computer terminal GPS, and recovering a detector after detection is finished;
2) taking out the SD card, reading and processing data, and extracting X, Y, Z triaxial axial acceleration and angular acceleration recorded by the MEMS in the detector;
3) reproducing the flow path of the detector, and drawing X, Y, Z a time-varying line graph of the three-axis axial acceleration and the angular acceleration;
4) matching the processed information with the established black box model through a time series three-dimensional track model, and diagnosing abnormal operation conditions in the drainage pipe network;
the data stored in the SD card comprise time data, 3D acceleration data and 3D angular acceleration data, wherein the 3D acceleration data comprise g x 、g y 、g z The 3D angular acceleration data comprises w x 、w y 、w z The time data, the 3D acceleration data and the 3D angular acceleration data form a frame of data by 7 data, the detector collects and records the frame of data every 100ms, and the obtained 7 data are subjected to coordinate conversion and displacement compensation;
in the coordinate conversion process, the collected and processed 3D acceleration is subjected to primary integration to obtain the moving speed, then secondary integration is carried out to obtain the displacement, and the displacement is popularized to a three-dimensional space t n The motion displacement along the directions of the x, y and z axes of the acceleration sensor at the moment is respectively as follows:
wherein v (n) is t n Instantaneous speed at time, a (n) being t n Instantaneous acceleration at the time, s (n) is 0 to t n Time interval displacement, namely connecting spatial position coordinate points at all moments in a three-dimensional coordinate system to obtain a spatial motion track;
the coordinate conversion is to obtain a space track, convert the measured acceleration into a constant reference coordinate system, and set the geographic coordinate system ox of the target object n y n z n And the carrier coordinate system of the sensor is ox b y b z b And the transformation relation obtained by the rotation sequence of the Z, Y and X axes from the geographic coordinate system to the carrier coordinate system is as follows:
in the formula
Is a rotation matrix, i.e. a direction cosine matrix;
the black box model building method comprises the steps of building different black box models according to different unconventional operation states of siltation, collapse, leakage and peripheral groundwater infiltration, building a multi-dimensional black box model with the pipe diameter, the siltation height, the siltation volume, the flow rate and the motion track changed in the siltation state, building a multi-dimensional black box model with the pipe diameter, the sublevel height and the motion track changed in the pipe disjointed state, and building a black box model with the water leakage state which comprises parameters such as the pipe diameter, the damage area and the motion track; the water leakage comprises the situations of leakage-in and leakage-out;
In the time series three-dimensional track pattern matching process, because the motion track is a multi-point pattern which changes along with time, the time series point pattern matching problem of the three-dimensional space is taken as the problem analysis in the two-dimensional space, the point set in the whole three-dimensional space is respectively projected to XOY, YOZ and XOZ planes, the track similarity distance measurement function is respectively calculated for the two-dimensional point set on the three planes, and finally the feature similarity of two points in the three-dimensional space is obtained by adding the feature similarities of the three two-dimensional spaces, and the method adopts the two-dimensional point set pattern matching;
the two-dimensional point set pattern matching comprises the following steps:
s1: let T be a trajectory of a point consisting of two-dimensional coordinates representing a position, defined as a candidate trajectory in the database, T ═ s 1 ，s 2 ，...s n }，s i (i is more than or equal to 1 and less than or equal to n) represents a two-dimensional coordinate point, and the length of the track T is n; defining the target track as q ═ t 1 ，t 2 ，...t n Given a trajectory T, its sub-trajectories from i point to j point are T [ i, j]＝s i ，s i+1 ，...s j ；
S2: for a target trajectory point set q ═ t 1 ，t 2 ，...t n And a trace point t k E.g. q, trace point t k And sub-track T [ i, j]The shortest distance between them is:
wherein d(s) h ，t k ) Representing points of track s h And the locus point t k The euclidean distance therebetween;
The time sequence three-dimensional track mode matching defines two function distances for judging in order to match the tracks, and the sum of the function distances is mainly to calculate each track point T in each segment pair target track point set q of the candidate tracks T j The sum of the shortest distances of the target track point set q and the candidate track T is measured according to the sum of the shortest distances of the target track point set q and the candidate track T; the maximum function distance (MaxDis) is to calculate each track point T in the target track point set q of each segment pair of the candidate track T j Is used as the distance between the target track point set q and the candidate track T,
definition 1: and functional distance:
definition 2: maximum function distance:
wherein 1 is less than or equal to l 1 ≤…≤l i ≤l i+1 ≤n；
S3: the process is realized by adopting pruning dynamic programming, the target track q has m points, the candidate tracks have n points, the m distances need to be compared, and the m distances need to be compared through a distance detection function in sequence.
2. The method for diagnosing and detecting the running state of the drainage pipe network according to claim 1, wherein the displacement compensation is to firstly normalize the collected acceleration data, convert the attitude quaternion calculated last time into a carrier coordinate system to obtain a gravity unit vector of the current carrier coordinate system, and the error vector of each quantity is the error between the attitude after the integral of the gyroscope and the attitude measured by the accelerometer, and is used for correcting the gyroscope, namely:
E(n)＝E(n-1)+K i ×e
g(n)＝g(n-1)+K p ×e+E(n)
Wherein g is (g) x ，g y ，g z ) For gyroscope angular velocity data, by adjusting K i And K p Both parameters enable a fast correction of the gyroscope angular velocity data by means of acceleration data,
according to the ideal condition that the speed before the motion starts and after the motion stops is zero, the acceleration and the displacement before the motion starts are also zero, namely v r (0)＝0，v r (k e )＝0，a r (0)＝0，s r (0)＝0，k e For the motion termination time, the compensation polynomial for velocity and displacement is:
wherein: v (k), s (k) are calculation results before reconstruction; v is r (k)、s r (k) The speed and displacement after reconstruction; and a (0), v (0) and s (0) are system initial states.
3. The device for diagnosing and detecting the running state of the drainage pipe network by adopting the method according to any one of claims 1-2, which is characterized by comprising a bottom cover (1) and a top cover (9), wherein the bottom cover (1) and the top cover (9) form a sealed detector inner space through a waterproof sealing ring (10), and the inner space sequentially comprises a counterweight system, an MEMS gyroscope acceleration system, a starting system and a polymer lithium battery (8) from bottom to top;
the counterweight system comprises an x-direction eccentric counterweight block (2) and a bottom cover integral counterweight block (3); the MEMS gyroscope acceleration system comprises an MEMS gyroscope accelerometer (4) and a GPS chip (5) which are arranged on the same SD memory card; the starting system comprises a starter (6) and a starter fixing groove (7); the MEMS gyroscope accelerometer adopts a high-precision gyroscope accelerometer WT61SD and is a six-axis module; when debugging is carried out, debugging information is required to be output through a serial port, the acceleration of the MEMS gyroscope and the USB-TTL are well connected, and then the MEMS gyroscope is inserted into a computer; the SD memory card has 16G or 32G storage capacity.
4. The device for diagnosing and detecting the operating condition of the drain pipe network according to claim 3, wherein the overall height of the device is 2 cm.
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